docs-v2/content/influxdb/v2.0/reference/flux/stdlib/experimental/to.md

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experimental.to() function The `experimental.to()` function writes data to an InfluxDB v2.0 bucket. The function structures data differently than the built-in `to()` function.
influxdb_2_0_ref
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experimental.to Experimental
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/v2.0/reference/flux/stdlib/built-in/outputs/to/

The experimental.to() function writes data to an InfluxDB v2.0 bucket, but in a different structure than the built-in to() function.

Function type: Output

import "experimental"

experimental.to(
  bucket: "my-bucket",
  org: "my-org"
)

// OR

experimental.to(
  bucketID: "1234567890",
  orgID: "0987654321"
)

Expected data structure

Data structure expected by built-in to()

The built-in to() function requires _time, _measurement, _field, and _value columns. The _field column stores the field key and the _value column stores the field value.

_time _measurement _field _value
timestamp measurement-name field key field value

Data structure expected by experimental to()

experimental.to() requires _time and measurement columns, but field keys and values are stored in single columns with the field key as the column name and the field value as the column value.

_time _measurement field_key
timestamp measurement-name field value

If using the built-in from() function, use pivot() to transform data into the structure experimetnal.to() expects. See the example below.

Parameters

bucket

The bucket to write data to. bucket and bucketID are mutually exclusive.

Data type: String

bucketID

The ID of the bucket to write data to. bucketID and bucket are mutually exclusive.

Data type: String

org

The organization name of the specified bucket. Only required when writing to a different organization or a remote host. org and orgID are mutually exclusive.

Data type: String

orgID

The organization ID of the specified bucket. Only required when writing to a different organization or a remote host. orgID and org are mutually exclusive.

Data type: String

Examples

Use pivot() to shape data for experimental.to()
import "experimental"

from(bucket: "example-bucket")
  |> range(start: -1h)
  |> pivot(
      rowKey:["_time"],
      columnKey: ["_field"],
      valueColumn: "_value")
  |> experimental.to(
      bucket: "bucket-name",
      org: "org-name"
  )